那上面结果的最后一行是什么意思啊？是不是检验随机效应是否显著阿？

真的没人知道吗？

使用 clogit 命令，与xtlogit是等价的，但 clogit会给出 Pseudo R2

. clogit union age grade south, group(id)
note: multiple positive outcomes within groups encountered.
note: 2894 groups (11559 obs) dropped due to all positive or
      all negative outcomes.
note: grade omitted due to no within-group variance.

Iteration 0:   log likelihood = -2851.7589 
Iteration 1:   log likelihood = -2845.4946 
Iteration 2:   log likelihood = -2845.4861 
Iteration 3:   log likelihood = -2845.4861 

Conditional (fixed-effects) logistic regression   Number of obs   =       7665
                                                  LR chi2(2)      =      44.93
                                                  Prob > chi2     =     0.0000
Log likelihood = -2845.4861                       Pseudo R2       =     0.0078

------------------------------------------------------------------------------
       union |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         age |   .0135179   .0052148     2.59   0.010     .0032972    .0237387
       south |  -1.055083    .173377    -6.09   0.000    -1.394895   -.7152703
------------------------------------------------------------------------------

. xtlogit union age grade south, fe
note: multiple positive outcomes within groups encountered.
note: 2894 groups (11559 obs) dropped due to all positive or
      all negative outcomes.
note: grade omitted due to no within-group variance.

Iteration 0:   log likelihood = -2851.7589 
Iteration 1:   log likelihood = -2845.4946 
Iteration 2:   log likelihood = -2845.4861 
Iteration 3:   log likelihood = -2845.4861 

Conditional fixed-effects logistic regression   Number of obs      =      7665
Group variable (i): idcode                      Number of groups   =      1254

                                                Obs per group: min =         2
                                                               avg =       6.1
                                                               max =        12

                                                LR chi2(2)         =     44.93
Log likelihood  = -2845.4861                    Prob > chi2        =    0.0000

------------------------------------------------------------------------------
       union |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         age |   .0135179   .0052148     2.59   0.010     .0032972    .0237387
       south |  -1.055083    .173377    -6.09   0.000    -1.394895   -.7152703
------------------------------------------------------------------------------

[此贴子已经被作者于2008-3-17 17:50:46编辑过]

看到 Pseudo R2       不还的用wald和LR判断吗。